Course Intro

Practical Computing Skills for Omics Data (PLNTPTH 5004)

Jelmer Poelstra

MCIC Wooster, Ohio State University

2025-08-26

Personal introductions

Introductions: Jelmer (instructor)

  • Lead of the CFAES Bioinformatics and Microscopy cores
    • Part of what was until recently called the Molecular & Cellular Imaging Center (MCIC)
    • We are now grouped under CFAES Analytical Resources, core facilities providing services in molecular biology, high-throughput sequencing, bioinformatics, microscopy, and soil analyses.
  • What I work on
    • The majority of my time is spent providing research assistance,
      working with grad students and postdocs on omics data
    • Teaching, such as this course, workshops, Code Club (https://osu-codeclub.github.io)

  • Background in animal evolutionary genomics & speciation

  • In my free time, I enjoy bird watching – locally & all across the world

Introductions: TA / co-instructor

TBA

Introductions: You

  • Name

  • Lab and Department

  • Research interests and/or current research topics

  • Something about you that is not work-related, such as a hobby or fun fact

Course overview

The core goals of this course

Learning skills to:

  • Do your research more reproducibly and efficiently (e.g. by using code)

  • Work with large-scale “omics” datasets

TBD: explain focus on fundamental computation skills

Course background: Reproducibility

Two related ideas:

  1. Getting same results with an independent experiment (replicable)

  2. Getting same results given the same data (reproducible)

Our focus is on #2.

Course background: Reproducibility (cont.)

“The most basic principle for reproducible research is: Do everything via code.”
—Karl Broman


Additionally, also important for reproducibility are:

  • Project organization and documentation (week 3)

  • Sharing your data and code (for code: Git & GitHub, week 4)

  • How you code (covered throughout)


Another motivator: working reproducibly will benefit future you!

Course background: Efficiency and automation

  • Using code enables you to work more efficiently and automatically —
    particularly useful when having to:

    • Do repetitive tasks

    • Recreate a figure or redo an analysis after adding a sample

    • Redo a project after uncovering a mistake in the first data processing step.

Course background: Omics data

  • Omics data is increasingly important in biology, and most notably includes:
    • Genomics
    • Transcriptomics
    • Proteomics
    • Metabolomics

The next lecture will introduce omics data in a bit more details.

What this course does and does not focus on

  • While we’ll be using some example omics datasets, this course will not comprehensively cover the analysis of omics data — our focus is more on fundamental computational skills.

  • A highly recommended follow-up course to learn omics data analysis specifics:
    Genome Analytics (HCS 7004) by Jonathan Fresnedo-Ramirez

What is bioinformatics?

Also: computational biology

TBA

Course topics

The Unix shell & shell scripts

Being able to work in the Unix shell is a fundamental skill in computational biology.

  • You’ll spend a lot of time with the Unix shell, starting this week, and including in weeks that aren’t solely focused on the shell.
  • We’ll also write shell scripts, and will use an editor called VS Code for this and other purposes.

Bash (shell language)

VS Code

Project organization & documentation

Good project organization & documentation is a necessary starting point for reproducible research.

  • You’ll learn best practices for project organization, file naming, etc.

  • You’ll learn how to manage your data and software

  • To document and report what you are doing, you’ll use Markdown files.


Markdown

Version control with Git and GitHub

Using version control, you can more effectively keep track of project progress, collaborate, share code, revisit earlier versions, and undo.

  • Git is the version control software we will use,
    and GitHub is the website that hosts Git projects (repositories).

  • You’ll also use Git + GitHub to hand in your graded assignments.


High-performance computing with OSC

Thanks to supercomputer resources, you can work with very large datasets at speed — running up to 100s of analyses in parallel, and using much larger amounts of memory and storage space than a personal computer has.

  • We will use OSC throughout the course, and you’ll get a brief intro to it today.
  • In week 5, you’ll learn how to manage data and software at OSC.
  • In week 6, we’ll learn to submit shell scripts as OSC “batch jobs” with Slurm.


Automated workflow management

Using a workflow written with a workflow manager, you can run and rerun an entire analysis pipeline with a single command, and easily change and rerun parts of it, too.

  • We’ll use the workflow language Nextflow to build our pipelines
  • You will also learn how to use comprehensive, best-practice omics data Nextflow pipelines produced by the nf-core initiative

R

  • DETAILS TBA

  • Address R vs Python

Using generative AI to help with coding

  • Details TBA


Course practicalities

Zoom

  • Be muted by default, but feel free to unmute yourself to ask questions any time.

  • Questions can also be asked in the chat.

  • Having your camera turned on as much as possible is appreciated!

  • “Screen real estate” — large/multiple monitors or multiple devices best.

  • Be ready to share your screen.

Websites & Books

  • Info about CarmenCanvas website…
  • The GitHub website contains:
    • Overviews of each week & readings
    • Slide decks and lecture pages
    • Exercises
    • Final project assignment information

  • Books:

    • Computing Skills for Biologists (“CSB”; Allesina & Wilmes 2019)
    • Bioinformatics Data Skills (“Buffalo”; Buffalo 2015)

Homework and grading

What your grade is made up of

You can earn a total of 100 points across 6 assignments and 4 final project checkpoints.

Graded assignments

These are due on Mondays and are worth 10 points each:

  1. Shell basics (week 3)
  2. Markdown & Git (week 5)
  3. Shell scripting (week 6)
  4. OSC batch jobs (week 8)
  5. Nextflow (week 11)
  6. R (week 14)

Final project

Plan and implement a small computational project, with the following checkpoints:

  • I: Proposal (due week 13 – 5 points)

  • II: Draft (due week 15 – 5 points)

  • III: Oral presentations on Zoom (week 16 – 10 points)

  • IV: Final submission (due Dec 15 – 20 points)



Data sets for the final project

If you have your own data set & analysis ideas, that is ideal. If not, I can provide you with this.

More information about the final project will follow later in the course.

Ungraded homework

  • Weekly readings — somewhat up to you when to do these, ideally before and after the lectures!

  • Weekly exercises — I recommend doing these on Fridays

  • Miscellaneous small assignments such as surveys and account setup.



Weekly materials & homework

I will try add the materials for each week on the preceding Friday — at the least the week’s overview and readings.

None of this homework had to be handed in.

Weekly recitation on Monday

We will have an optional but highly recommended weekly recitation meeting on Monday in which we go through the exercises for the preceding week.


Practice is key!

This course is intended to be highly practical and if you don’t practice the skills we will focus on by yourself, you will not get much out of it.


Please indicate your availability here: TBA

Rest of this week

  • Introduction to omics data
  • Introduction to the Ohio Supercomputer Center (OSC)

  • Homework:

    • TBA

Questions?





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